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  pages        = {35--52},
  year         = {2018},
  organization = {ACM}
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@inproceedings{boyle2016function,
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  booktitle    = {Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS)},
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  year         = {2016},
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% intro
@inproceedings{rathee2020cryptflow2,
  title        = {{CrypTFlow2}: Practical 2-party secure inference},
  author       = {Rathee, Deevashwer and Rathee, Mayank and Kumar, Nishant and Chandran, Nishanth and Gupta, Divya and Rastogi, Aseem and Sharma, Rahul},
  booktitle    = {Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS)},
  pages        = {325--342},
  year         = {2020},
  organization = {ACM}
}


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  booktitle    = {Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS)},
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  year         = {2017},
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@inproceedings{li2021crossline,
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  booktitle    = {Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS)},
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  year         = {2021},
  organization = {ACM}
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  year         = {2018},
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@inproceedings{dauterman2022waldo,
  title        = {Waldo: A private time-series database from function secret sharing},
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  booktitle    = {Proceedings of the IEEE Symposium on Security and Privacy (S\&P)},
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  year         = {2022},
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@inproceedings{jawalkar2024Orca,
  title        = {Orca: {FSS}-based Secure Training and Inference with {GPUs}},
  booktitle    = {Proceedings of the IEEE Symposium on Security and Privacy (S\&P)},
  author       = {Jawalkar, Neha and Chandran, Nishanth and Gupta, Kanav and Gupta, Divya and Basu, Arkaprava and Sharma, Rahul},
  year         = {2024},
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@inproceedings{kumar2020cryptflow,
  title        = {{CrypTFlow}: Secure tensorflow inference},
  author       = {Kumar, Nishant and Rathee, Mayank and Chandran, Nishanth and Gupta, Divya and Rastogi, Aseem and Sharma, Rahul},
  booktitle    = {Proceedings of the IEEE Symposium on Security and Privacy (S\&P)},
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  year         = {2020},
  organization = {IEEE}
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@inproceedings{liu2015oblivm,
  title        = {Oblivm: A programming framework for secure computation},
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@inproceedings{mohassel2017secureml,
  title        = {{SecureML}: A system for scalable privacy-preserving machine learning},
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  pages        = {19--38},
  year         = {2017},
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@inproceedings{rathee2021sirnn,
  title        = {{SIRNN}: A math library for secure {RNN} inference},
  author       = {Rathee, Deevashwer and Rathee, Mayank and Goli, Rahul Kranti Kiran and Gupta, Divya and Sharma, Rahul and Chandran, Nishanth and Rastogi, Aseem},
  booktitle    = {Proceedings of the IEEE Symposium on Security and Privacy (S\&P)},
  pages        = {1003--1020},
  year         = {2021},
  organization = {IEEE}
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@inproceedings{tramer2017sealed,
  title        = {Sealed-glass proofs: Using transparent enclaves to prove and sell knowledge},
  author       = {Tramer, Florian and Zhang, Fan and Lin, Huang and Hubaux, Jean-Pierre and Juels, Ari and Shi, Elaine},
  booktitle    = {Proceedings of the IEEE European Symposium on Security and Privacy (EuroS\&P)},
  pages        = {19--34},
  year         = {2017},
  organization = {IEEE}
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@inproceedings{chandran2019ezpc,
  title        = {{EzPC}: Programmable and efficient secure two-party computation for machine learning},
  author       = {Chandran, Nishanth and Gupta, Divya and Rastogi, Aseem and Sharma, Rahul and Tripathi, Shardul},
  booktitle    = {Proceedings of the IEEE European Symposium on Security and Privacy (EuroS\&P)},
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  year         = {2019},
  organization = {IEEE}
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@inproceedings{koti2021swift,
  title        = {{SWIFT}: Super-fast and robust Privacy-Preserving machine learning},
  author       = {Koti, Nishat and Pancholi, Mahak and Patra, Arpita and Suresh, Ajith},
  booktitle    = {Proceedings of the USENIX Security Symposium (USENIX Security)},
  pages        = {2651--2668},
  year         = {2021},
  organization = {USENIX Association}
}


@inproceedings{huang2022cheetah,
  title        = {Cheetah: Lean and fast secure two-party deep neural network inference},
  author       = {Huang, Zhicong and Lu, Wenjie and Hong, Cheng and Ding, Jiansheng},
  booktitle    = {Proceedings of the USENIX Security Symposium (USENIX Security)},
  pages        = {809--826},
  year         = {2022},
  organization = {USENIX Association}
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@inproceedings{juvekar2018gazelle,
  title        = {{GAZELLE}: A low latency framework for secure neural network inference},
  author       = {Juvekar, Chiraag and Vaikuntanathan, Vinod and Chandrakasan, Anantha},
  booktitle    = {Proceedings of the USENIX Security Symposium (USENIX Security)},
  pages        = {1651--1669},
  year         = {2018},
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@inproceedings{lu2023squirrel,
  title        = {Squirrel: A Scalable Secure Two-Party Computation Framework for Training Gradient Boosting Decision Tree},
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  booktitle    = {Proceedings of the USENIX Security Symposium (USENIX Security)},
  pages        = {6435--6451},
  year         = {2023},
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@inproceedings{patra2021aby2,
  title        = {{ABY2.0}: Improved Mixed-Protocol secure Two-Party computation},
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  booktitle    = {Proceedings of the USENIX Security Symposium (USENIX Security)},
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  year         = {2021},
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  year         = {2017},
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@inproceedings{patra2020blaze,
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  year         = {2020},
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@inproceedings{cheng2019strongly,
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@inproceedings{zhuo2016privacy,
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  booktitle    = {Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)},
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@inproceedings{jin2017centurion,
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%------------------------------------------------------


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%------------------------------------------------------






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%------------------------ not used ------------------------------

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